# ADVANCED ECONOMETRICS 1 - 2019/0

Module code: ECOD003

Module Overview

The module provides the analytical tools required for deriving the limiting distribution of estimators in the context of linear models (OLS and instrumental variables) and nonlinear models (NLS and Generalized Method of Moments).  Since asymptotic approximations may be not be sufficiently accurate in finite samples, we study how to construct bootstrap critical values, in order to provide more accurate inference.

Module provider

Economics

Number of Credits: 0

ECTS Credits: 0

Framework: FHEQ Level 8

JACs code: L140

Module cap (Maximum number of students): N/A

Module Availability

Semester 1

Prerequisites / Co-requisites

None.

Module content

Indicative content includes:

• Statistics tools: Modes of Convergence, Law of Large Numbers, Central Limit Theorems

• Consistency and Asymptotic Normality of Ordinary Least Squares Estimators

• Hypothesis Testing: Wald, Lagrange Multiplier and Likelihood Ratio Tests

• Estimation of Asymptotic Covariance Matrices

• Instrumental Variables Estimators: (1) Consistency and Asymptotic Normality, (2) Weak instruments and weak identification

• Consistency and Asymptotic Normality of Generalized Method of Moments Estimators (GMM), Tests for Identifying Restrictions

• The Bootstrap and its Applications

Assessment pattern

Assessment type Unit of assessment Weighting
School-timetabled exam/test TAKE HOME TEST 30
Examination EXAM 70

Alternative Assessment

None.

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate their technical skills relating to the use of econometrics techniques to conduct innovative empirical work.

Thus, the summative assessment for this module consists of:

A three- hour final examination

A take home examination involving matlab programming and theoretical exercises, typically due in week 9.

Formative assessment

Due to the limited size of the cohort and the level of study, formal formative assesment is replaced with informal discussions during and outside lectures.

Feedback

Student will receive verbal feedback during the lectures and tutorials through direct interaction, as well more formally following coursework submission.

Module aims

• Provide students with an advanced understanding of key statistical and econometric tools
• Enable student to state a hypothesis of interest and derive a test
• Enable students to be competent and innovative econometrics users

Learning outcomes

Attributes Developed
001 Understand econometrics papers in top general and top field journals. KC
002 Formalise hypotheses of interest KCT
003 Modify existing tests/estimators to accommodate their own econometrics problems KCT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Independent Study Hours: 117

Lecture Hours: 22

Tutorial Hours: 11

Methods of Teaching / Learning

The learning and teaching strategy is designed to: develop student independent research skills, by training them to conduct critical analysis of papers in scientific journals. Problems sets will be assigned to ensure all concepts and methods are properly mastered.

The learning and teaching methods include:

Interactive lectures. Review of problem set solutions.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.